Multiple Imputation using Weighted Quantile Sum Analysis
Consider a set/mixture of continuous, correlated, and censored components/chemicals that are reasonable to combine in an index and share a common outcome. These components are also interval-censored between zero and upper thresholds, or detection limits, that may be different among the components. The `miWQS` package applies the multiple imputation (MI) procedure to the weighted quantile sum regression (WQS) methodology for continuous, binary, or count outcomes. In summary, MI consists of three stages: (1) imputation, (2) analysis, and (3) pooling. First, the missing values are imputed by bootstrapping (Lubin et.al (2004) <10.1289>), Bayesian imputation, or placing the below the detection limits in the first quantile (BDLQ1) (Ward et.al. (2014) <10.1289>). Second, the estimate.wqs() function implements WQS regression if the components are complete, imputed, or missing (Carrico et.al. (2014) <10.1007>) . If the data is missing, BDLQ1 is automatically implemented. Lastly, the pool.mi() function calculates the pooled statistics according to Rubin's rules (Rubin 1987).10.1007>10.1289>10.1289>
*First Release of Package to the public.
*For updates to CRAN team, see cran-comments.
- Replaced examples using example dataset in package instead of using package wqs. Looks cleaner
- Remove printed output from estimate.wqs.
- Made documentation from estimate.wqs clearer.
- Cleaned up print.wqs documentation
- Reworked plot.wqs() function by using ggplot2 instead of base plotting in R.
- Fixed bug in doing Poisson Rate WQS regressions. Added argument offset to the check_function() and randomize.train()
- For updates to CRAN team, see cran-comments.
- Added a
NEWS.md file to track changes to the package.
- First Release of the Package to CRAN team
- Successfully passed windows check.